Multi-turn Conversation and Prompts for Regulatory Affairs

Data Characteristics in This Category

Pharmacovigilance data for regulatory submissions primarily originates from clinical trial reports, real-world evidence (RWE) data, global adverse event databases for marketed drugs, and safety updates issued by regulatory agencies. Data update frequency can vary; during clinical trials, submissions may be periodic, while post-market, updates are continuous and dynamic. Document structures are diverse, including structured Case Report Forms (CRFs), semi-structured medical literature abstracts, and unstructured safety update reports or regulatory communications. Fields and units are highly specialized. For example, "adverse event terms" typically follow MedDRA coding, "dosage" includes units like mg/kg or mg/day, and "observation time" is expressed in days or weeks.

Constraints Imposed by These Characteristics on "Multi-turn Conversation and Prompts"

Highly structured and specialized MedDRA coding requires the conversation system to accurately identify and map user input, preventing semantic deviations. Extracting key information from unstructured reports demands stronger contextual understanding and entity recognition capabilities from prompts to precisely capture drugs, adverse reactions, and patient characteristics from long texts. The dynamic nature of data updates requires the system to integrate the latest information promptly, ensuring the timeliness and accuracy of conversation content. Furthermore, complex dosage and time units necessitate robust numerical understanding and calculation abilities from prompts. The system must handle unit conversions and dosage correlation analysis to ensure the rigor of submission materials.

Configuration Settings

Configuration Item	Suggested Value	Rationale for This Value
`maxContext`	`2000-4000` characters	Regulatory documents often contain detailed descriptions, requiring longer context to understand complex medical information.
`Similarity threshold` (Similarity Threshold)	`0.75-0.85`	Ensures recalled knowledge snippets are highly relevant to specialized terms, reducing false positives.
`Recall count` (Recall Count)	`5-8` items	Balances accuracy with response speed, covering main relevant knowledge points.
`Chunk size` (Segment Length)	`500-800` characters	Balances semantic completeness with retrieval efficiency, avoiding the splitting of critical information.
`Chunk Overlap Length` (Segment Overlap Length)	`100-150` characters	Ensures segment continuity and preserves contextual coherence, especially in medical descriptions.
`maxToken`	`2048-4096`	Allows the model to generate detailed medical explanations and submission recommendations, meeting professional depth.

Three Common Pitfalls

Conversation results lack critical medical terminology or dosage information. This occurs when prompts do not adequately guide the model to extract specific fields or when maxContext is not configured to be long enough.
The system encounters a 500 error when processing medical literature abstracts. This happens if the input file encoding or format does not fall within the default processing range of PARSE_FILE_TIMEOUT_SECONDS, or if the text length exceeds the processing limit.
After multiple turns of conversation, the system fails to provide accurate subsequent submission recommendations based on previous patient information. This indicates that the context transfer mechanism between multiple AI conversation modules in the workflow is incorrectly configured, leading to information loss.

How to Verify Correct Configuration

Simulate multiple typical multi-turn conversations for regulatory submissions to check if the system can accurately identify MedDRA codes and maintain contextual consistency.
Upload real clinical trial reports containing complex dosage units and observation times. Verify if the system can correctly extract and process this specialized data.
In the conversation logs, check if the maxContext length is effectively utilized and if the knowledge snippets recalled under the Similarity threshold (Similarity Threshold) consistently remain highly relevant.

Note: The values provided are common starting points. Measure them against your own samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.